Peripheral Immune Signatures in Alzheimer Disease
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
According to the current paradigm, the main cause of AD is the accumulation of neurotoxic amyloid beta (Aβ) peptide aggregates resulting from the cleavage of the amyloid precursor protein into peptides of different length, with the 42 amino acid long Aβ42 being the most toxic form. Aβ can aggregate and form plaques in the brain. It further promotes the hyperphosphorylation of the tau protein which forms characteristic neurofibrillary tangles and thereby loses its important role in axonal transport and contributes to neurodegeneration. Therefore, treatments have targeted Aβ, but clinical trials of immunotherapies caused severe side effects and showed that Aβ clearance alone did not result in any cognitive improvement. This leads to the question: what else promotes AD pathology? Here, we review data on systemic inflammation and the possible roles that the immune system might play in AD. Microglia and astrocytes are activated and secrete inflammatory cytokines and chemokines. Via a disturbed blood-brain barrier, peripheral immune cells are activated and recruited towards inflamed brain lesions and amyloid plaques, but due to the chronic nature of the amyloid burden and their reduced function, these cells are not able to control inflammation and the associated detrimental immune responses. In addition, age-related inflammation and chronic infection with herpes viruses might contribute to the systemic inflammation and exacerbate attempts to restore the balance of inflammation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it